Revista Brasileira de Informática na Educação, Volume 24, Número 2, 2016
An Ontology Framework to Apply Gamification in
CSCL Scenarios as Persuasive Technology
Geiser Chalco Challco
University of São Paulo, ICMC
São Carlos, SP, Brazil
[email protected]
Resumo
Riichiro Mizoguchi
Japan Advanced Institute of Science and
Technology, JAIST
Nomi, Asahidai, Japan
[email protected]
O uso de scripts de Aprendizagem Colaborativa com Suporte Computacional é uma
abordagem eficaz para apoiar interações significativas e melhorar a aprendizagem.
Infelizmente, em algumas situações, a colaboração scripts diminue a motivação e
engajamento dos alunos, o que torna mais difícil seu uso ao longo do tempo. Para lidar com
esse problema, propomos aplicar Gamificação como Tecnologia Persuasiva para induzir a os
alunos a seguirem os scripts de maneira adequada sem a sensação de obrigação, evitando
dessa forma os problemas de motivição. Para atingir este objetivo, é necessário um
conhecimento exaustivo da Gamificação e seu impacto na aprendizagem colaborativa. Assim,
estamos desenvolvendo uma ontologia para proporcionar uma sistematização formal do
conhecimento sobre a gamificação e sua adequada aplicação em cenários de aprendizagem
colaborativa. Neste artigo, nos concentramos na formalização dos conceitos básicos
relacionados à gamificação como Tecnologia Persuasiva em cenários de Aprendizagem
Colaborativa. Além disso, para demonstrar a aplicabilidade da nossa abordagem,
apresentamos um estudo de caso, no que construímos e aplicamos um modelo de gamificação
personalizado com base nas estruturas ontológicas definidas aqui.
Palavras-Chave: Gamification, Tecnologia Persuasiva,
Aprendizagem Colaborativa, Cenários de Aprendizagem, Scripts.
Abstract
Seiji Isotani
University of São Paulo, ICMC
São Carlos, SP, Brazil
[email protected]
Estratégias
Persuasivas,
The use of Computer-Support Collaborative Learning (CSCL) scripts is an effective approach
to support meaningful interactions and better learning. Unfortunately, in some situations,
scripted collaboration decreases the motivation and engagement of students, which makes
more difficult to use it over time. To deal with this problem, we propose to apply gamification
as Persuasive Technology (PT) to induce the students to follow the scripts in the proper way
without the sensation of obligation, avoiding in this way the motivation problems. To achieve
this goal, it is necessary an exhaustive knowledge on gamification and its impact on
Collaborative Learning (CL). Thus, we are developing an ontology to provide a formal
systematization of the knowledge on gamification and its proper application in CL scenarios.
In this paper, we focus in the formalization of basic concepts related to gamification as a PT in
CL scenarios. Furthermore, to demonstrate the applicability of our approach, we present a
case study, where we built and apply a personalized gamification model based on the
ontological structures defined here.
Keywords: Gamification, Persuasive Technology, Persuasive Strategies, Collaborative
Learning, Learning Scenarios, Scripts.
DOI: 10.5753/RBIE.2016.24.02.67
Challco et al.
1 Introduction
Despite the successful use of Computer-Support
Collaborative Learning (CSCL) scripts to support the
design and execution of collaborative learning activities,
there are situations in which these scripts may lead to
motivation problems [4], [8], [13]. Sometimes, a learner
neglects his behavior and does not perform the task as it
is requested by the script because he wants to complete
the task quickly, and other times, the lack of choice with
respect to the next interaction in the sequencing of
activities increases the sense of obligation.
The motivation problems in the CSCL scripts
negatively influence the learners’ attitudes and
behaviors, degrade classroom group dynamics, and result
in a long-term negative learning outcomes [8]. To deal
with these problems, in recent years, the researches and
practitioners are seeing gamification at possible solution
to motivate and engage the students in learning scenarios
[16]. However, gamification may fail, primarily due to
poor design [27], because its effects depend greatly on
the context in which this technology is applied [12].
Thus, our goal is to build a formal framework to support
the design of Collaborative Learning (CL) scenarios in
which game elements will be introduced and customized
for each student. This customization will increase the
chances of learners to willingly follow the tasks and roles
defined in a script with a positive attitude and behavior.
Instead to set the same game elements with the same
settings for all students, we need to adequately customize
each of them for each student. This task will be done by
selecting the proper game elements and deploying them
with particular settings to motivate and persuade the
students to complete CL activity in a proper way. Thus,
it is necessary to take into consideration the theories of
game design, motivation, and human behavior, where
the
specialists
describe
how
psychological,
anthropological and pedagogical factors influence the
people and connect them with proper instructional and
learning theories to identify the expected benefits
provided by the gamification in CSCL contexts [11],
[16], [23], [28].
To support the development of well-thought-out
gamified CL scenarios based on these theories, we
propose a formalization of the knowledge described on
them represented as an ontology framework, referred to
as (hidden for pair revision). In this paper, we propose
to extend it by adding concepts and semantic relations
related to gamification from the perspective of applying
Persuasive Technology (PT) in CL scenarios in order to
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motivate and persuade students to interact properly while
learning in group. We define Gamification as
Persuasive Technology (PT) as “the use of game design
elements for the purpose to change peoples’ attitudes
and behaviors through persuasion and social influence
without using coercion and/or deception.”
The paper is divided as follows, in Section 2, we
present the related works. Section 3 presents the
methodology employed to conduct this research and
detail the ontological structures obtained by the
methodology. In Section 4, we present a case study to
describe how the ontological structures proposed in this
paper can be used to build a personalized gamification
model, and how this new model may be applied in the
context of a CSCL Script. Finally, Section 5 presents the
conclusion and future steps.
2 Related Works
In the past years, Gamification has been applied by
many researches in different educational contexts [13].
However, in some of these scenarios, gamification is
applied as pointification or exploitationware, focusing
heavily on the rewards and making the learning
scenarios more stressful instead of more enjoyable. In the
literature, there are few models and frameworks that
help instructional designers to choose proper game
elements for different scenarios [4][7]. These studies
proposed gamification frameworks that link game
elements with different factors related to the
environment and users, such as the psychological,
cultural anthropological, and pedagogical preferences of
people. For example, in Bunchball [2], it is proposed a
framework that relates game mechanics to human
desires, in which each game mechanics satisfies a set of
human desires.
Currently, to the best of our knowledge, there are no
other ontologies have been proposed to specifically
provide computational models or frameworks to gamify
CL scenarios. Thus, our work propose to extends the
achievements of the different models and frameworks for
gamification. We will extend these works, proposing a
set of concepts in a formal ontology that can be used by
humans and computers to gamify CL scenarios.
3 Ontological Frameworks
To establish the concepts and semantic relations
related to the gamification as PT, we used an ontology
engineering approach [20] and the model of roles [21].
The ontological structures in this ontology have been
Challco et al.
developed using the Hozo Ontology editor [18], and they
are available at http://ontogacles.caed-lab.com
The current formalization of our ontology defines the
concepts and terms shown in Figure 1 (a), where: I-mot
goal is the individual motivational goal of the person in
focus (I). These goals should be satisfied by the use of a
motivational strategy (e.g. the changes in the individual
psychological needs, motivational state, attitudinal state,
and/or behavioral state). I-player role is the player role
defined for the person in focus (I). You-player role is the
player role defined for the person (You) who is
interacting with the person in focus (I). Y<=I-mot goal
is the motivational strategy (Y<=I-goal) that enhances
the learning strategy defined for the student in focus (I).
And, the term I-gameplay is the gameplay strategy that
defines the rational arrangement of player role,
motivational strategy and game elements for the student
in focus (I). W(A)-gameplay is the CL gameplay that
represent the interactions between students and game
elements. Figure 1 (b) shows as example the space of
interactions for the students LA and LB.
Figure 1: Concepts and terms defined in gamified CL scenarios.
3.1 Models to Personalize Gamification
In previous papers [13]–[15], we defined a set of
ontological structures showed in Figure 2 using the
concepts presented in Figure 1. These structures allow
for the creation of ontological models to personalize the
gamification of CL scenarios by properly defining player
roles and game elements for each student and
considering their individual differences.
Figure 2: Ontological structures to represent a gamified CL scenario.
To represent gamified CL scenarios, the ontological
An Ontology Framework to Apply Gamification
in CSCL Scenarios as Persuasive Technology
structure shown in Figure 2 (a) extends the structure to
represent CL scenarios proposed in [13]–[15]. In our
new ontological structure, the motivational strategy
(Y<=I-mot goal) is the application of gamification for
the student in focus (I). This strategy has the goal of
motivate, engage and persuade the student (I) to follow
the intended learning behavior defined by a script. Thus,
the motivational strategy is a concept that indicates how
a student “I” can use a specific player role (I-player role)
to interact with another student “You” that plays a
complementary role (You-player role) in order to
achieve his/her individual motivational goals (I-mot
goal).
Figure 2 (b) shows the ontological structure for
player roles that allows us to classify the students in
different player types. This classification depends on the
necessary and desire condition. In the current version of
our ontology, these conditions are: the individual
personality preferences (e.g. playing styles, cognitive
styles), current motivational states, and current
psychological needs.
The ontological structure shown in Figure 2 (c)
represents the gameplay strategy for the student in focus
(I), in which the arrangement is defined by the player
role of student in focus “I” (Primary focus), the player
role of student “You” who is interacting with the student
“I” (Secondary focus), the motivational strategy for the
student “I” (S<=P-mot goal), the motivational strategy
for the student “You” (P<=mot goal), and the game
elements for the student “I” (What use) in a Gamified CL
Scenario.
Employing the ontological structures of Figure 2, we
can define ontological models to personalize the
gamification in CL scenarios. Due to space limitation,
we do not detail the complete process of how to build
these models. Nevertheless, an extended description of
this process is presented in [4].
To give a concrete example about how to use the
defined ontology, in Figure 3, we describe the
gamification concepts extracted from Marczewski’s
framework [19] as classes in our model. According to
Marczewski, there are four basic intrinsic player types
(also called user types): achiever, socializer,
philanthropist, and free-spirit. These player types are
user intrinsic motivated by the needs of mastery,
relatedness, purpose, and autonomy, respectively.
Socializers want to interact with others players to satisfy
the need of relatedness. Thus, game elements based on
social
mechanics
guilds/teams,
as
social
network/connections, social status, social discovery,
social pressure and competition are defined as essential
in the framework for the engagement of socializers.
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Challco et al.
Based on the information of socializers provided by
Marczewski’s framework, Figure 3 shows the ontological
structure that can be used to define gamified CL
scenarios for students who can be socializers. Based on
the assertion of Marczewski’s who says that the
“Socializer wish to interact with people and the
socializer’s reward is knowing who is interacting with
him/her”1, we define in Figure 3 (a) that there is a
motivational strategy of “gamifying for socializes”
(Y<=I-mot goal) in which the relatedness need (I-mot
goal) of a student (I) who plays the socializer role is
satisfied by interacting with other socializer. Next, in the
CL scenario that is being gamified, we define that there
is a possible gameplay strategy (I-gameplay) to
implement the “gamifying for socializers” in which it is
necessary the use of the game elements “social status”
and “social connection” to provide information about
who is interaction with who.
RBIE V.24 N.2 – 2016
organization of the concepts related to the PSs and their
application based on the Fogg’s Behavior Model [9] and
Skinner’s Operant Conditioning theory [26]. In this
work, we extend our previous work making them
independent of any model and theory for the
structuration and organization of the PSs and their
application. Thus, Figure 4 (a) shows our new
ontological structures to represent a gamified CL
scenario in which it is applied the PSs using game
elements. In these structures, we redefine the role of CL
process for a role of CL Gameplay, where the
ontological structure W(A)-gameplay (CL process)
replaces the structure W(A)-goal (CL Gameplay).
Figure 4: Ontological structures to represent a gamified CL scenarios in
which it is applied the persuasive strategies using game elements.
Figure 3: Ontological structures for socializers in a CL scenario.
The structure shown in Figure 3 (b) represents that
for a student to play the socializer role, it is necessary
that he/she must to be intrinsic motivated with the
relatedness need, and it is desirable that he/she has the
individuality personality preferences of interacting in the
game (interacting-orientation) with other players (userorientation). And the gameplay strategy shown in Figure
3 (c) defines that the student “I” (who plays the role of
socializer, Primary focus) must employ the gameplay
strategy Gamifying for socializer (S<=P-mot goal) to
interacting with the student “You” (who plays the
socializer role, Secondary focus).
3.2 Models to Apply Gamification as PT
In the paper [3], we define an ontological model that
allow us to represent the application of Persuasive
Strategies (PSs) using game elements in CL scenarios. In
this previous work, we proposed the structuration and
1
Appendix of Possible Interactions for the player types, available at
http://www.gamified.uk/user-types/ (Retrieved in January, 2016)
70
While the I-gameplay (Gameplay strategy) defines
“what are the best game elements for each student,” the
W(A)-gameplay (CL Gameplay) defines the “how to use
the selected game element to attain the individual
motivational goals.” As the game dynamic is the runtime behavior of game mechanics acting on player inputs
over time [24], the CL Gameplay of a gamified CL
scenario is composed by a set of CL Game dynamics that
define the “How to play” for the students who become
players, and their necessary and complementary
interactions become Gamified Influential I_L events, see
Figure 4 (a).
In the CL ontology [13]–[15], each interaction of a
CSCL script is represented by two parts: an instructional
event and a learning event. Thus, in our ontology, the
Gamified Influential I_L event is also composed by two
parts: a gamified instructional event and a gamified
learning event. Because Sicart [25] defines the game
mechanics as methods invoked by agents (e.g. players,
and artificial enemies) designed for the interaction with
the game state, the instructional and learners events (I/L
events) play the role of game mechanics event in the
ontological structure for a gamified instructional event
and a gamified learning events that is shown in Figure 4
Challco et al.
(b). When an instructional or learning event becomes a
game mechanics event, as we can see in the ontological
structure, the instructor or learner plays the player role,
his/her actions become game mechanics, and a set of
individual motivational goals (I-mot goal) are included
as “benefits for the player” in this structure.
The gamified instructional and learning events do not
only contain the intended learning behavior defined by a
CSCL script as actions for students who play the role of
instructor or learner. In the gamified instructional and
learning events, as it is shown in Figure 5, we also
describe two kind of actions:
 Game actions that “stimulate” the students to do the
actions that are defined in the instructional and
learning events; and
An Ontology Framework to Apply Gamification
in CSCL Scenarios as Persuasive Technology
components produce changes in the students’ attitudes
and behaviors. These actions follow the students’ actions
to reinforce the intended learning behavior defined by
the script. Thus, the game actions defined in these game
events allow the students to attain the individual
motivational goals.
3.3 Example: Gamified Influential I_L events
Employing our formalization of Gamified Influential
I_L events proposed here (in this paper), Figure 6 shows
an example of representation and ontological structure
for the application of PS “reward” over the interaction
“instigating discussion,” where we detailed the elements
for the gamified learning event “expose opinion.” The
reward strategy consists in giving positive feedback to
increase the frequency of a target behavior [10].
 Game actions as game effects that follow the actions
defined in the instructional and learning events and
allow the students to “attain” their individual goals.
Figure 5: Elements in a gamified influential I_L event.
Based on theories of humans behavior and learning,
the game actions defined in gamified instructional and
learning events are structuring and organizing into:
game stimulus events and game consequence events.
In a gamified CL scenario, the proper stimulus given
by actions of game agents using game components at the
right moment tells and leads the students to carry out the
intended learning behavior in a predictable way. For us,
the game components are the basic parts of the game
world layer manipulated by the game agents [1], such as
points, badges, and virtual coins. As shown in Figure 5,
the game agents (G1 and G3), their actions that stimulate
the
students’
actions
(defined
in
the
instructional/learning events), and the game components
used by these agents are represented in game events that
play the role of game stimulus events in the ontological
structure of gamified instructional and learning events.
As it is shown in Figure 5, the gamified instructional
and learning events also contain game events that play
the role of game consequence events, where the actions
(as game effects) of game agents (G2 and G4) using game
Figure 6: Example for the application of persuasive strategy “reward”
over the interaction “instigating discussion.”
In our example showed in Figure 6, the game events
are structure based on the Fogg’s Behavior Model [9]
and Skinner’s reinforcement theory [26]. According to
Fogg’s Behavior Model, for a behavior to occur, the
motivation, ability, and trigger must converge at the
same moment reaching the activation threshold. Thus, as
it is shown in Figure 6 (a), the game stimulus event
becomes a trigger event, and the application of the PS
“reward” in the learning event “expose opinion” to
persuade a learner to do an “counter-argument” consists
in promise points (Game action) that must be done by a
point system (Game agent) using the points (Game
component). Figure 6 (b) shows the ontological structure
to represent this fact as a game event that plays the role
of trigger event.
According to Skinner, the change in the learners’
attitudes and behaviors is learned by operant
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Challco et al.
RBIE V.24 N.2 – 2016
conditioning, where the consequences of humans’
actions modify the tendency to repeat a behavior. Thus,
as it is shown in Figure 6 (a), the game stimulus
becomes a operant conditioning event for the application
of the PS “reward” over the learning event “expose
opinion.” As result, the action “give point” (Game
action) becomes a positive reinforcement for the counterargue of a student who plays the role of learner. This
action is done by the point system (Game agent) using
the points (Game component) and allows the learner to
satisfy his need of mastery (Benefits for the player).
Finally, Figure 6 (b) shows the ontological structure to
represent this fact as a game event that plays the role of
operant conditioning event.
P-Player\
S-Player
Achiever
Conqueror
Daredevil
Achiever
COOP, REWD,
SEMT/SUGG
COOP, REWD,
SEMT/SUGG
x
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
COOP, REWD,
SEMT/SUGG
x
SIML
Conqueror
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
x
COOP, REWD,
SEMT/SUGG
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
x
SIML
Daredevil
SIML
x
COOP, REWD,
SEMT/SUGG
SIML
x
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
SIML
Mastermind
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
x
COOP, REWD,
SEMT/SUGG
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
x
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
x
SIML
Seeker
CUST, PERS,
CMPT/CMPR,
PRAS
x
COOP, REWD,
SEMT/SUGG
CUST, PERS,
CMPT/CMPR,
PRAS
x
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
CUST, PERS,
CMPT/CMPR,
PRAS
x
SIML
Socializer
COOP,
CMPT/CMPR
x
COOP, REWD,
SEMT/SUGG
COOP,
CMPT/CMPR
x
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
COOP,
CMPT/CMPR
x
SIML
Survivor
SEMT/SUGG,
CMPT/CMPR
x
COOP, REWD,
SEMT/SUGG
SEMT/SUGG,
CMPT/CMPR
x
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
SEMT/SUGG,
CMPT/CMPR
x
SIML
4 Case Study
In this section, to demonstrate the applicability of our
ontological structures presented in the previous section,
we built a personalized gamification model that allow us
to apply gamification as PT. To build this model, we
employed the information defined by Orji et al. in [22].
In the first step to build our model of gamification,
we made the Table 1 through the combination of PSs for
each pair of player roles (gamer types). Thus, the
students who play the role of instructor become primary
players (P-Player), and the students who play the role of
learner become secondary players (S-Player) in a CL
scenario.
Mastermind
COOP, REWD,
SEMT/SUGG
x
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
x
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
SIML
x
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
CUST, PERS,
CMPT/CMPR,
PRAS
x
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
COOP,
CMPT/CMPR
x
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
SEMT/SUGG,
CMPT/CMPR
x
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
Seeker
Socializer
Survivor
COOP, REWD,
SEMT/SUGG
x
CUST, PERS,
CMPT/CMPR,
PRAS
COOP, REWD,
SEMT/SUGG
x
COOP,
CMPT/CMPR
COOP, REWD,
SEMT/SUGG
x
SEMT/SUGG,
CMPT/CMPR
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
x
CUST, PERS,
CMPT/CMPR,
PRAS
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
x
COOP,
CMPT/CMPR
CMPT/CMPR,
SIML, PERS,
SEMT/SUGG
x
SEMT/SUGG,
CMPT/CMPR
SIML
x
CUST, PERS,
CMPT/CMPR,
PRAS
SIML
x
COOP,
CMPT/CMPR
SIML
x
SEMT/SUGG,
CMPT/CMPR
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
x
CUST, PERS,
CMPT/CMPR,
PRAS
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
x
COOP,
CMPT/CMPR
SEMT/SUGG,
CMPT/CMPR,
PERS, SIML,
CUST
x
SEMT/SUGG,
CMPT/CMPR
CUST, PERS,
CMPT/CMPR,
PRAS
CUST, PERS,
CMPT/CMPR,
PRAS
x
COOP,
CMPT/CMPR
CUST, PERS,
CMPT/CMPR,
PRAS
x
SEMT/SUGG,
CMPT/CMPR
COOP,
CMPT/CMPR
x
CUST, PERS,
CMPT/CMPR,
PRAS
COOP,
CMPT/CMPR
CMPT/CMPR
x
SEMT/SUGG,
CMPT/CMPR
SEMT/SUGG,
CMPT/CMPR
x
CUST, PERS,
CMPT/CMPR,
PRAS
SEMT/SUGG,
CMPT/CMPR
x
CMPT/CMPR
SEMT/SUGG,
CMPT/CMPR
CMPT/CMPR: competition & comparison, COOP: cooperation, CUST: customization, PERS: personalization, PRAS: praise, SEMT/SUGG: self-monitoring & suggestion,
SIML: simulation, REWD: reward
Table 1: Player roles and Persuasive Strategies to Gamify influential I_L events
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Challco et al.
An Ontology Framework to Apply Gamification
in CSCL Scenarios as Persuasive Technology
During the building of the Table 1, we verified if the
PSs have negative influence (contra-persuasive) between
the primary and secondary player roles. For example, the
competition/comparison and cooperation strategies are
contra-persuasive for students who play the player roles
of daredevil and survivor, respectively. To simplify our
example, we also define that it is not possible to use the
PSs of cooperation and competition/comparison in the
same CL scenario at the same time, selecting the most
persuasive for students who play the primary player role
(P-Player). Finally, we indicated these restriction in the
Table 1 by a line (strikethrough) in the PSs.
gives 10 points to the student plays become a game
consequence event. During the step (1), the action of the
progress bar system plays the role of game consequence
event through the game action of “give suggestion.”
Finally, after step (n) when the learner will received
acceptance, the “unlockable system” plays the game
consequence event through game action of “unlock
content.”
As it is shown in Table 1, our model also proposes
that we will be able to employ more than one PS using
game elements to influence changes in the attitudes and
behaviors of students. For example, the first row defines
that the PSs “cooperation,” “reward” and “selfmonitoring & suggestion” can be used to apply
gamification as PT for a group of students who play the
player role of achiever, if there is no one student who
plays the player role of survivor.
Based in the combination of PSs defined in Table 1,
the second step consists in developing storyboards that
shows how each strategy is applied using the different
game elements. Thus, for example, Figure 6 shows a
storyboard illustrating how the combination for the PSs
of “cooperation,” “reward” and “self-monitoring &
suggestion” is applied in a CL scenario using the game
components of “unlockable content,” “points” and
“progression bar.” These game components are
respectively employed by the game agents “unlockable
system,” “point system” and “progress bar system” to
persuade a student to “guide other students towards the
solution of problems” (intended learning behavior).
According to the storyboard, during the step (0),
when the game actions of “show condition to unlock
content” and “give suggestion” are respectively done by
the “unlockable system” and “progress bar system” to
stimulate the student to take the action of “give
information about how to solve a problem” then them
become game stimulus events. After the students gives
the information, during the step (1), the point system
Persuasive
Strategy
COOP
CMPT
/CMPR
Game event as stimulus event
Game
Game action
Component
Unlockable
Show condition
content
Virtual item
Show condition
Leaderboard
Status
Display scores
Rank progress
Figure 6: Storyboard illustrating the cooperation (top), reward (middle)
and self-monitoring & suggestion (bottom) strategies in a CL scenario.
In the third step, employing the storyboards that
represent the application of PSs using game elements, we
develop and formalized a set of game events that play the
role of stimulus and consequence events. Due to space
limits, Table 2 only shows a partial list of these game
events that we are currently developing. The complete
list is available at https://goo.gl/lkaU8O, and it is open to
discussion and future changes.
The fourth and last step is the definition of
ontological structures that represent the application of
PSs using game elements for each interaction of a CSCL
script. Thus, we extend the ontological structures shown
in Figure 4 employing the information of Table 1 and
Table 2.
Game event as consequence event
Game
Game action
Changes in
Component
(effect)
game state
Unlockable
Unlock
Increase
content
content
contents
Give virtual
Increase
Virtual items
item as gift
virtual items
Virtual items
REWD
Virtual items
Promise
virtual items
Virtual items
Give random
item
Give virtual
items
Increase
virtual items
Increase
virtual items
Observation of
game design
Communal
discovery
Social fabric
of games
Leaderboard
Envy
Lottery
Virtual items
73
Challco et al.
RBIE V.24 N.2 – 2016
Points
Promise points
PRAS
Virtual item
SEMT
/SUGG
Progress bar
Highlight
obtained
virtual items
Give
suggestion
Points
Increase
points
Give points
Points
Pride
Progress bar
Increase
progress
Change
percentage
Achievement
Table 2: Mapping of Persuasive Strategies to game events (partial list)
Applying Personalized Gamification Model
Figure 7 shows the ideal flow to gamify CL scenarios
from the viewpoint of an instructional designer who
employs an intelligent theory aware system based on our
ontology. In the first step (1), the designer sets the
proper player roles and game elements for each student.
In the second step (2), he designs the CL gameplay as a
set of CL game dynamics employing the player roles and
game elements that were set in the first phase. Finally, in
the third step (3), the designer makes an interaction
analysis over the obtained gamified CL scenarios. The
purpose of third step is identify if the game events and
their influences are adequate or not, proposing better
solutions by the meaningful result obtained during the
run-time of a CSCL script.
In the paper [4], we defined a pseudo-algorithm to set
the proper player roles and game elements according to
current motivational stage, psychological needs, and
individual personality preferences that are defined as
necessary and desired condition in the structure “player
role,” see Figure 2 (b). After this setting, in a authoring
tool, the next step (2) is the use of ontological structures
W(A)-gameplay defined in the personalized gamification
model to define the CL gameplay that will be employed
in a gamified CL scenario. The definition of this CL
gameplay will be made employing the ontological
structures of CL Game dynamic and Gamified Influential
I_L events. As example, Table 3 shows the definition of
a CL gameplay for a CSCL script based on the theory of
Cognitive Apprenticeship, in which the player role of
“achiever” is defined for students “l1” and “l2” who play
the role of “master”, and the player role of “apprentice”
is defined for students “l3” and “l4” who play the role of
socializer. In this CL scenario, the PSs of “cooperation”
(COOP), “reward” (REWD), and “self-monitoring &
suggestion” (SEMT/SUGG) are applied for the
interactions of students who play the role of achiever.
And the PS of “cooperation” (COOP) is applied for the
interactions of students who play the role of socializer.
Figure 7: Flow to gamify CL scenarios in semantic web authoring tools
I_L events
(I event/L event)
(n) Setting up the
learning context
(Giving information
/ Receiving
information)
(n) Demonstrate how
to solve problem
(Demonstration /
Observing
demonstration)
Persuasive
Strategies
COOP
REWD
SEMT/SUGG
COOP
REWD
SEMT/SUGG
COOP
(n) Monitoring
(Checking / Being
checked)
REWD
SEMT/SUGG
(d) Clarifying the
problem
74
COOP
Game Events for Masters:
l1 and l2 (Achiever role)
Unlockable system - Unlockable content:
(Show condition / )
Point system - Points: (Promise points /
Give points – add 10 points)
Progress bar system - Progress bar: (Give
suggestion / Increase Progress - add 10%)
Unlockable system - Unlockable content:
(Show condition / )
Point system - Points: (Promise points /
Give points – add 100 points)
Progress bar system - Progress bar: (Give
suggestion / Increase Progress - add 40%)
Unlockable system - Unlockable content:
(Show condition / )
Point system - Points: (Promise points /
Give points – add 10 points)
Progress bar system - Progress bar: (Give
suggestion / Increase Progress - add 40%)
Unlockable system - Unlockable content:
(Show condition / )
Game Events for Apprentices:
l3 and l4 (Socializer role)
Unlockable system - Unlockable
content: (Show condition / )
Unlockable system - Unlockable
content: (Show condition / )
Unlockable system - Unlockable
content: (Show condition / )
Unlockable system - Unlockable
content: (Show condition / )
Challco et al.
(Identifying
learner’s problem /
Externalization of
problem)
(d) Notifying how
the learner is
(Giving information
/ Receiving
information)
(d) Instigating
thinking
(Argumentation /
Receiving arguments)
(d) Requesting
problem’s details
(Asking problematic
understanding /
Externalization of
problems)
(d) Showing a
solution
(Explanation /
Receiving
explanation)
(n) Affirmative
reaction
(Acceptance /
Receiving
acceptance)
An Ontology Framework to Apply Gamification
in CSCL Scenarios as Persuasive Technology
SEMT/SUGG
COOP
REWD
SEMT/SUGG
COOP
REWD
SEMT/SUGG
COOP
SEMT/SUGG
COOP
REWD
SEMT/SUGG
COOP
SEMT/SUGG
Progress bar system - Progress bar: (Give
suggestion / )
Unlockable system - Unlockable content:
(Show condition / )
Point system - Points: (Promise points /
Give points – add 10 points)
Progress bar system - Progress bar: (Give
suggestion / )
Unlockable system - Unlockable content:
(Show condition / )
Point system -Points: (Promise points /
Give points – add 10 points)
Progress bar system - Progress bar: (Give
suggestion / )
Unlockable system - Unlockable content:
(Show condition / )
Unlockable system - Unlockable
content: (Show condition / )
Unlockable system - Unlockable
content: (Show condition / )
Unlockable system - Unlockable
content: (Show condition / )
Progress bar system - Progress bar: (Give
suggestion / )
Unlockable system - Unlockable content:
(Show condition / )
Point system - Points: (Promise points /
Give points – add 100 points)
Progress bar system - Progress bar: (Give
suggestion / )
Unlockable system - Unlockable content: (
/ Unlock content – Increase new problems)
Unlockable system - Unlockable
content: (Show condition / )
Unlockable system - Unlockable
content: ( / Unlock content –
Increase new problems)
Progress bar system - Progress bar: ( /
Increase Progress - add 10%)
Table 3: Game events to Gamify a CL scenario based on the theory “Cognitive Apprenticeship”
In the Table 3, the necessary interaction has the
prefix “(n),” the desire interaction has the prefix of “(d),”
and the game event is represented by the form “GA GC: GAs/GAe – C,” where GA is a game agent, GC is
the game component, GAs is the game action for a game
stimulus event, GAe is the game action defined as game
effect for a game consequence event, and C is the change
in the game world layout as result of this game effect.
5 Conclusion an Future Research
In this paper, we presented a set of ontological
structures that will able the organization and
formalization of knowledge related to gamification as PT
in CL scenarios. We defined the concept related to CL
gameplay, CL game dynamic, and gamifed influential
I_L event. These structures allow us to formalize and
structure the application of PSs using game elements for
each interaction of CSCL scripts. To demonstrate the
applicability of our approach, we developed an
ontological personalized gamification model in Section
5. This model was based on the mapping of PSs and
game features proposed by Orji et al. in [22]. And we
believe that the results of this work are the first steps in
order to create new semantic web authoring tools that
will provide assistance and recommendation to gamify
CL scenarios, making them more motivating and
engaging to the learners. Our next steps are the
formalization of concepts related to Flow Theory [6],
player's journey [7], and fun for game design [17].
Acknowledgements
The authors would like to thank CAPES, CNPq and
FAPESP for supporting this research.
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